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Record W4235212611 · doi:10.2196/preprints.17354

Designing a Virtual Reality Game for Promoting Empathy Toward Patients With Chronic Pain: Feasibility and Usability Study (Preprint)

2019· preprint· en· W4235212611 on OpenAlexaff
Xin Tong, Diane Gromala, Seyedeh Pegah Kiaei Ziabari, Chris Shaw

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldNeuroscience
TopicPain Management and Placebo Effect
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsEmpathyPerspective (graphical)UsabilityPsychologyVirtual realityStigma (botany)Chronic painApplied psychologyMedicineSocial psychologyComputer sciencePsychiatryHuman–computer interaction

Abstract

fetched live from OpenAlex

BACKGROUND Many researchers have been evaluating how digital media may impact the emotional and perspective taking aspects of empathy in both clinical and nonclinical settings. Despite the growing interest in using virtual reality (VR) and VR games to motivate empathy, few studies have focused on empathy for people who live with chronic pain. OBJECTIVE Chronic pain affects, by conservative estimates, 1 in 5 people in industrialized countries. Despite this prevalence, public awareness of chronic pain was remarkably low until the recent opioid crisis; as a result, stigma remains a problem frequently faced by people who live with this condition. To address this, the VR game AS IF was developed to increase nonpatients’ empathy toward the growing number of people who live with long-term chronic pain. On the basis of our prior work, we overhauled our approach, designed and built a VR prototype and evaluated it, and offered design suggestions for future research. METHODS We introduced the design features of the VR game AS IF and described the study we devised to evaluate its effectiveness. We adopted a mixed methods approach and compared the empathy-related outcomes in both pre- and posttesting. A total of 19 participants were recruited. RESULTS The findings of this study suggest that the VR game was effective in improving implicit and explicit empathy as well as its emotional and perspective taking aspects. More specifically, for the Empathy Scale, the total pretest scores (mean 47.33, SD 4.24) and posttest scores (mean 59.22, SD 4.33) did not reach statistical significance (P=.08). However, we did find differences in the subscales. The kindness subscale showed a statistically significant increase in the posttest score (mean 15.61, SD 2.85) compared with the pretest score (mean 17.06, SD 2.65;P=.001). For the Willingness to Help Scale, a significant increase was observed from a t test analysis (P<.001) of scores before (mean 7.17, SD 2.28) and after (mean 8.33, SD 2.03) the gameplay. The effect size for this analysis was large (d=−1.063). CONCLUSIONS The contributions of this research are as follows: AS IF provides a promising approach for designing VR games to motivate people’s empathy toward patients with chronic pain, the study evaluates the potential effectiveness of such a VR approach, and the general design suggestions devised from this study could shed light on future VR game systems.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.066
GPT teacher head0.305
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2019
Admission routes1
Has abstractyes

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